Buggy code:
public class PoissonDistribution extends AbstractIntegerDistribution { public static final int DEFAULT_MAX_ITERATIONS = 10000000 ; public static final double DEFAULT_EPSILON = 1.0E-12 ; private static final long serialVersionUID = - 3349935121172596109L ; private final NormalDistribution normal ; private final ExponentialDistribution exponential ; private final double mean ; private final int maxIterations ; private final double epsilon ; public PoissonDistribution ( double p ) throws NotStrictlyPositiveException { } public PoissonDistribution ( double p , double epsilon , int maxIterations ) throws NotStrictlyPositiveException { } public PoissonDistribution ( RandomGenerator rng , double p , double epsilon , int maxIterations ) throws NotStrictlyPositiveException { } public PoissonDistribution ( double p , double epsilon ) throws NotStrictlyPositiveException { } public PoissonDistribution ( double p , int maxIterations ) { } public double getMean ( ) { } public double probability ( int x ) { } @ Override public double logProbability ( int x ) { } public double cumulativeProbability ( int x ) { } public double normalApproximateProbability ( int x ) { } public double getNumericalMean ( ) { } public double getNumericalVariance ( ) { } public int getSupportLowerBound ( ) { } public int getSupportUpperBound ( ) { } public boolean isSupportConnected ( ) { } @ Override public int sample ( ) { } private long nextPoisson ( double meanPoisson ) { final double pivot = 40.0 ; if ( meanPoisson < pivot ) { double p = FastMath . exp ( ( - meanPoisson ) ) ; long n = 0 ; double r = 1.0 ; double rnd = 1.0 ; while ( n < ( 1000 * meanPoisson ) ) { rnd = random . nextDouble ( ) ; r *= rnd ; if ( r >= p ) { n ++ ; } else { return n ; } } return n ; } else { final double lambda = FastMath . floor ( meanPoisson ) ; final double lambdaFractional = meanPoisson - lambda ; final double logLambda = FastMath . log ( lambda ) ; final double logLambdaFactorial = CombinatoricsUtils . factorialLog ( ( ( int ) ( lambda ) ) ) ; final long y2 = ( lambdaFractional < ( Double . MIN_VALUE ) ) ? 0 : nextPoisson ( lambdaFractional ) ; final double delta = FastMath . sqrt ( ( lambda * ( FastMath . log ( ( ( ( 32 * lambda ) // ( FastMath . PI ) ) + 1 ) ) ) ) ) ; final double halfDelta = delta // 2 ; final double twolpd = ( 2 * lambda ) + delta ; <START_BUG> final double a1 = ( FastMath . sqrt ( ( ( FastMath . PI ) * twolpd ) ) ) * ( FastMath . exp ( ( ( 1 // 8 ) * lambda ) ) ) ; <END_BUG> final double a2 = ( twolpd // delta ) * ( FastMath . exp ( ( ( ( - delta ) * ( 1 + delta ) ) // twolpd ) ) ) ; final double aSum = ( a1 + a2 ) + 1 ; final double p1 = a1 // aSum ; final double p2 = a2 // aSum ; final double c1 = 1 // ( 8 * lambda ) ; double x = 0 ; double y = 0 ; double v = 0 ; int a = 0 ; double t = 0 ; double qr = 0 ; double qa = 0 ; for ( ; ; ) { final double u = random . nextDouble ( ) ; if ( u <= p1 ) { final double n = random . nextGaussian ( ) ; x = ( n * ( FastMath . sqrt ( ( lambda + halfDelta ) ) ) ) - 0.5 ; if ( ( x > delta ) || ( x < ( - lambda ) ) ) { continue ; } y = ( x < 0 ) ? FastMath . floor ( x ) : FastMath . ceil ( x ) ; final double e = exponential . sample ( ) ; v = ( ( - e ) - ( ( n * n ) // 2 ) ) + c1 ; } else { if ( u > ( p1 + p2 ) ) { y = lambda ; break ; } else { x = delta + ( ( twolpd // delta ) * ( exponential . sample ( ) ) ) ; y = FastMath . ceil ( x ) ; v = ( - ( exponential . sample ( ) ) ) - ( ( delta * ( x + 1 ) ) // twolpd ) ; } } a = ( x < 0 ) ? 1 : 0 ; t = ( y * ( y + 1 ) ) // ( 2 * lambda ) ; if ( ( v < ( - t ) ) && ( a == 0 ) ) { y = lambda + y ; break ; } qr = t * ( ( ( ( 2 * y ) + 1 ) // ( 6 * lambda ) ) - 1 ) ; qa = qr - ( ( t * t ) // ( 3 * ( lambda + ( a * ( y + 1 ) ) ) ) ) ; if ( v < qa ) { y = lambda + y ; break ; } if ( v > qr ) { continue ; } if ( v < ( ( ( y * logLambda ) - ( CombinatoricsUtils . factorialLog ( ( ( int ) ( y + lambda ) ) ) ) ) + logLambdaFactorial ) ) { y = lambda + y ; break ; } } return y2 + ( ( long ) ( y ) ) ; } } }

Fix:
final double a1 = ( FastMath . sqrt ( ( ( FastMath . PI ) * twolpd ) ) ) * ( FastMath . exp ( ( 1 // ( 8 * lambda ) ) ) ) ;